Abstract: In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.
Abstract: This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.
Abstract: Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables. In
order to effectively control the air-ratio, this paper presents a model
predictive fuzzy control algorithm based on online least-squares
support vector machines prediction model and fuzzy logic optimizer.
The proposed control algorithm was also implemented on a real car for
testing and the results are highly satisfactory. Experimental results
show that the proposed control algorithm can regulate the engine
air-ratio to the stoichiometric value, 1.0, under external disturbance
with less than 5% tolerance.
Abstract: The Proton Exchange Membrane Fuel Cell (PEMFC)
control system has an important effect on operation of cell.
Traditional controllers couldn-t lead to acceptable responses because
of time- change, long- hysteresis, uncertainty, strong- coupling and
nonlinear characteristics of PEMFCs, so an intelligent or adaptive
controller is needed. In this paper a neural network predictive
controller have been designed to control the voltage of at the
presence of fluctuations of temperature. The results of
implementation of this designed NN Predictive controller on a
dynamic electrochemical model of a small size 5 KW, PEM fuel cell
have been simulated by MATLAB/SIMULINK.
Abstract: This paper investigates the control of a bouncing
ball using Model Predictive Control. Bouncing ball is a benchmark
problem for various rhythmic tasks such as juggling, walking,
hopping and running. Humans develop intentions which may be
perceived as our reference trajectory and tries to track it. The
human brain optimizes the control effort needed to track its
reference; this forms the central theme for control of bouncing ball
in our investigations.
Abstract: In this paper a stochastic scenario-based model predictive control applied to molten salt storage systems in concentrated solar tower power plant is presented. The main goal of this study is to build up a tool to analyze current and expected future resources for evaluating the weekly power to be advertised on electricity secondary market. This tool will allow plant operator to maximize profits while hedging the impact on the system of stochastic variables such as resources or sunlight shortage.
Solving the problem first requires a mixed logic dynamic modeling of the plant. The two stochastic variables, respectively the sunlight incoming energy and electricity demands from secondary market, are modeled by least square regression. Robustness is achieved by drawing a certain number of random variables realizations and applying the most restrictive one to the system. This scenario approach control technique provides the plant operator a confidence interval containing a given percentage of possible stochastic variable realizations in such a way that robust control is always achieved within its bounds. The results obtained from many trajectory simulations show the existence of a ‘’reliable’’ interval, which experimentally confirms the algorithm robustness.
Abstract: Enhancement of the performance of a reverse osmosis
(RO) unit through periodic control is studied. The periodic control
manipulates the feed pressure and flow rate of the RO unit. To ensure
the periodic behavior of the inputs, the manipulated variables (MV)
are transformed into the form of sinusoidal functions. In this case, the
amplitude and period of the sinusoidal functions become the
surrogate MV and are thus regulated via nonlinear model predictive
control algorithm. The simulation results indicated that the control
system can generate cyclic inputs necessary to enhance the closedloop
performance in the sense of increasing the permeate production
and lowering the salt concentration. The proposed control system can
attain its objective with arbitrary set point for the controlled outputs.
Successful results were also obtained in the presence of modeling
Abstract: In this paper a neural adaptive control method has
been developed and applied to robot control. Simulation results are
presented to verify the effectiveness of the controller. These results
show that the performance by using this controller is better than
those which just use either direct inverse control or predictive
control. In addition, they show that the resulting is a useful method
which combines the advantages of both direct inverse control and